Cannabidivarin: current evidence on pharmacological activity, biopharmaceutical limitations and clinical prospects - Surojit Banerjee; Debadri Banerjee; Veerma Ram; Hitesh Kulhari; Deep Pooja; Anupama Singh; Vikas Anand Saharan (Phytochemistry Reviews, 2026)

A Digital Twin Ocean: can we improve coastal ocean forecasts using targeted marine autonomy? - Dale Partridge; Deep Banerjee; David Ford; Ke Wang; Jozef Skákala; Juliane Wihsgott; Prathyush P. Menon; Susan Kay; Daniel Clewley; Andrea Rochner; Emma Sullivan; Matthew Palmer (Ocean Science, 2026)
This study outlines the development and testing of a Digital Twin Ocean (DTO) framework, aimed at improving coastal ocean forecasts through the use of autonomous underwater gliders. A fleet of gliders were deployed in the western English Channel during August-September 2024 to collect measurements of temperature, salinity, chlorophyll and oxygen, aiming to track the movement of the harmful algal bloom Karenia mikimotoi. Measurements were assimilated into a very high resolution (1.5 km) numerical forecast model, with an implementation of biogeochemistry data assimilation for this purpose. The model forecast was then used by a probabilistic uncertainty model to plan a series of waypoints to navigate the glider fleet towards features of interest. By utilising a continuous feedback loop of measurement, prediction, guidance, and refinement a system with real time coupling between the real ocean environment and its digital counterpart has been established. Building upon a prior pilot study of Ford et al. (2022), this work improves every element of the system to address several limitations of the prior configuration. Whilst a bloom was present in the wider area, measurements and modeling suggest it didn't enter the glider operation zone. Despite this and other operational challenges the mission clearly demonstrates the benefits of such a system. The ability to simultaneously track multiple features of interest, namely chlorophyll maxima and oxygen minima, would not have been possible with a single glider resulting in significant benefits to the system. Furthermore, the improvement to biogeochemical forecasting has been demonstrated through a series of post mission experiments, highlighting the advantages of high temporal resolution observations and increased spatial resolution of the model.

Modelling primary production: multitude of theories, or multitude of languages? - Jozef Skákala; Shubha Sathyendranath; Yuri Artioli; Deep S. Banerjee; Heather Bouman; Robert J. W. Brewin; Momme Butenschön; Stefano Ciavatta; Stephanie Dutkiewicz; Yanna Fidai; David Ford; Grinson George; et al. (Ocean Science, 2026)
Marine primary production, converting approximately 50 Gt of inorganic carbon into organic carbon per year, is an important component of the global carbon cycle, and a major determinant of past, present and future climate. Large-scale, long-term estimates of marine primary production rely primarily on two types of models: satellite-based models that make extensive use of remote-sensing data, and ecosystem models providing numerical simulation of ecological processes embedded in general ocean circulation models. Intercomparison exercises of model outputs (both within and across the two model types) have consistently revealed high discrepancies between estimated global ocean primary production, including divergent magnitudes and even opposite trends. Model-observation comparisons are also complex, because paucity of data, differences in measurement techniques, and evolving methodologies could all lead to difficulties with the interpretation of results. These uncertainties limit the applications of primary production models (both satellite-based and ecosystem), especially in the climate context, where an important question is whether climate change will drive significant future changes in regional or global primary production. Both satellite-based and ecosystem models rely on a range of fixed model parameters, whose values need to be carefully estimated and tested. In this paper, we suggest that such model parameters represent an underappreciated but important source of inter-model differences. With the proliferation of both satellite and in situ observations of relevant variables at global scales, and the availability of powerful statistical tools such as data assimilation and machine learning, we argue that time is right to systematically examine model parameters, gaining both better insights into parameter values and how those values might vary in space and time. We argue that such spatio-temporal parameter variability can be theoretically justified for ecosystem models with complexity similar to those commonly used within Earth System Models (ESMs) in climate studies. The spatially and temporally varying parameter values could serve to unify models that are structurally different. An important aspect of this unification could be the ability to infer the spatio-temporal variability of parameters in the less complex models from the emergent behaviour of the more complex ones. This could include ecosystem model simulations of nutrients, temperature, phytoplankton classes, or vertical distributions informing satellite-based models. We conclude that better understanding of model parameter roles and integration (or inter-calibration) of different types of models could reduce discrepancies among the primary production models and improve the reliability of marine primary production projections.

Marine data assimilation in the UK: the past, the present, and the vision for the future - Jozef Skákala; David Ford; Keith Haines; Amos Lawless; Matthew J. Martin; Philip Browne; Marcin Chrust; Stefano Ciavatta; Alison Fowler; Daniel Lea; Matthew Palmer; Andrea Rochner; et al. (Ocean Science, 2025)
In the last 2 decades, UK research institutes have led a wide range of developments in marine data assimilation (MDA), covering areas from operational applications in physics and biogeochemistry to fundamental theory. We highlight the emergence of strong collaboration in the UK MDA community over this period and the increasing unification of its tools. We focus on identifying the MDA stakeholder community and current/future areas of impact, as well as current trends and future opportunities. This includes the rapid growth of machine learning (ML)/artificial intelligence (AI) and digital-twin applications. We articulate a vision for the future, including the need for future types of observational data (whether planned missions or hypothetical) and how the community should respond to increases in computational power and new computer architectures (e.g. exascale computing). We contrast the requirements of different MDA areas, including physics, biogeochemistry, and coupled data assimilation (DA). Although the specifics of the vision depend on each area, common themes emerge. We advocate for balanced redistribution of new computational capability among increased model resolution, model complexity, more sophisticated DA algorithms, and uncertainty representation (e.g. ensembles). We also advocate for integrated approaches, such as strongly coupled DA (ocean–atmosphere, physics–biogeochemistry, and ocean–sea ice) and the use of ML/AI components (e.g. for multivariate increment balancing, bias correction, model emulation, observation re-gridding, or fusion).

A comprehensive update on cannabidiol, its formulations and drug delivery systems - Surojit Banerjee; Vikas Anand Saharan; Debadri Banerjee; Veerma Ram; Hitesh Kulhari; Deep Pooja; Anupama Singh (Phytochemistry Reviews, 2025)

Bivariate sea-ice assimilation for global-ocean analysis–reanalysis - Andrea Cipollone; Deep Sankar Banerjee; Doroteaciro Iovino; Ali Aydogdu; Simona Masina (Ocean Science, 2023)
In the last decade, various satellite missions have been monitoring the status of the cryosphere and its evolution. Besides sea-ice concentration data, available since the 1980s, sea-ice thickness retrievals are now ready to be used in global operational prediction and global reanalysis systems. Nevertheless, while univariate algorithms are commonly used to constrain sea-ice area or volume, multivariate approaches have not yet been employed due to the highly non-Gaussian distribution of sea-ice variables together with the low accuracy of thickness observations. This study extends a 3DVar system, called OceanVar, which is routinely employed in the production of global/regional operational/reanalysis products, to process sea-ice variables. The tangent/adjoint versions of an anamorphosis operator are used to locally transform the sea-ice anomalies into Gaussian control variables and back, minimizing in the latter space. The benefit achieved by such a transformation is described. Several sensitivity experiments are carried out using a suite of diverse datasets. The sole assimilation of the CryoSat-2 provides a good spatial representation of thickness distribution but still overestimates the total volume that requires the inclusion of Soil Moisture and Ocean Salinity (SMOS) mission data to converge towards the observation estimates. The intermittent availability of thickness data can lead to potential jumps in the evolution of the volume and requires a dedicated tuning. The use of the merged L4 product CS2SMOS shows the best skill score when validated against independent measurements during the melting season when satellite data are not available. This new sea-ice module is meant to simplify the future coupling with ocean variables.

Are ocean-moored buoys redundant for prediction of Indian monsoon? - Maheswar Pradhan; Ankur Srivastava; Suryachandra A. Rao; Deep Sankar Banerjee; Abhisek Chatterjee; P. A. Francis; O. P. Sreejith; M. Das Gupta; V. S. Prasad (Meteorology and Atmospheric Physics, 2021)